Overview

Global identity verification and onboarding workflows increasingly span multiple languages, scripts, and cultural systems.

Financial institutions, payment providers, and other regulated entities face persistent challenges when interpreting non-Latin text including Chinese, Arabic, Cyrillic, Hebrew, and Indic scripts while preserving semantic meaning, compliance integrity, and identity accuracy.

Beltic Translation and Transliteration is an AI-driven linguistic intelligence layer designed to interpret, normalize, and standardize global text and document content.

Built atop large language and vision-language models, it enables real-time, context-aware processing of multilingual data with compliance-grade accuracy and traceability.

Core Capabilities

1. Multilingual Context Understanding

Conventional translation systems perform surface-level text conversion, often stripping away contextual nuance critical to identity and regulatory workflows.

Beltic’s architecture introduces semantic reasoning layers and LLM-based contextual evaluators that perform entity-aware interpretation.

These models:

• Preserve semantic and cultural meaning across language boundaries.

• Distinguish between personal names, legal entities, and geographic identifiers.

• Handle domain-specific financial and regulatory terminology (e.g., KYC, AML, PEP, UBO).

• Support bidirectional translation (Latin ↔ Non-Latin) while retaining the structural fidelity of original documents.

Impact:

This ensures that translated outputs maintain both lexical precision and contextual correctness essential for downstream verification, screening, and analytics pipelines.

2. Transliteration Engine

Beyond translation, Beltic’s transliteration subsystem converts non-Latin characters into standardized Latin-script equivalents, preserving phonetic accuracy and cross-system consistency.

Key capabilities include:

• Consistent name normalization across multilingual data sources.

• Cross-lingual matching in AML/KYC datasets, reducing false negatives caused by spelling variance.

• Dialect-sensitive phonetic mapping, distinguishing variants (e.g., Persian vs. Arabic forms).

• Adaptive transliteration profiles per jurisdiction or language standard (e.g., ISO 9 for Cyrillic, Hanyu Pinyin for Mandarin).

Impact:

This enables precise identity correlation across different alphabets and reduces manual remediation in compliance review pipelines.

3. AI-Powered Architecture

Beltic’s translation and transliteration framework integrates multiple AI modalities optimized for multilingual and multimodal data processing:

• Foundational Language Models (LLMs): Perform contextual inference and semantic translation.

• Vision-Language Models (VLMs): Enable text extraction and translation from scanned or image-based documents.

• Specialized OCR engines: Optimized for character segmentation and recognition in complex non-Latin scripts.

• Pre-processing layers: Detect, classify, and normalize script types before model inference.

• Post-processing pipelines: Apply linguistic rules, field-level normalization, and compliance formatting.

Each translation pipeline executes through validation checkpoints including:

• Confidence scoring and probabilistic validation.

• Automatic language identification.

• Entity-level and field-level consistency checks.

Impact:

This modular AI stack ensures deterministic reliability and traceable reasoning across diverse data and document sources.

Integration Across Beltic Ecosystem

The Translation and Transliteration Engine is deeply embedded into multiple Beltic product lines:

• Identity Verification: Enables document and data normalization across multilingual alphabets during onboarding.

• Document Extract: Converts OCR or parsed text into standardized multilingual output for LLM-driven analysis.

• Screening APIs: Aligns entities against global sanctions and watchlists regardless of language origin.

Impact:

This linguistic layer ensures seamless interoperability across Beltic’s ecosystem, maintaining a unified data representation for all users, languages, and document types.

Performance and Scaling

• Latency: Optimized for sub-second inference across common document and text classes.

• Coverage: Supports 100+ global languages and scripts, including right-to-left (RTL) systems.

• Accuracy: Validated on benchmark datasets for identity, regulatory, and financial contexts.